New energy locomotive big data management system and method

Abstract

The invention relates to a new energy locomotive big data management system and method, and belongs to the technical field of new energy locomotives, the new energy locomotive big data management system comprises a data acquisition module, a management module, a big data analysis module and a display module; the data acquisition module is used for determining a data acquisition interface and acquiring locomotive data in real time based on the data acquisition interface; the management module is used for managing the data acquisition interface and performing classified management on the locomotive data; the big data analysis module is used for constructing a big data model based on the locomotive data, predicting the health state and the health trend of the locomotive based on the big data model, obtaining prediction data, setting an alarm threshold value, and generating an early warning signal based on the alarm threshold value and the locomotive data; the display module is used for displaying the locomotive data, the prediction data and the early warning signal, unified management of the new energy locomotive is achieved, and efficient and safe operation of the new energy locomotive is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy locomotives, and in particular to a new energy locomotive big data management system and method. Background Art

[0002] New energy locomotives are a key project in the development strategy, covering new locomotives and the conversion of traditional locomotives from oil to electricity. The construction and transformation of new energy locomotives are currently in the initial stage, and urgently need the gradual accumulation and precipitation of a large amount of technical experience. In new energy locomotives, batteries and traction motors, as key equipment, their accumulation of experience occupies an extremely important position.

[0003] With the implementation of the new development strategy, substantial progress has been made in the new energy locomotive project. The amount of data from each project has grown explosively. These data contain enormous value. By managing and analyzing the data, the management of new energy locomotives can be achieved.

[0004] However, the traditional data processing model has been unable to adapt to the requirements of efficient management and analysis of large-scale and diversified data, and it is difficult to achieve unified management of new energy locomotives. Summary of the invention

[0005] In view of this, it is necessary to provide a new energy locomotive big data management system and method to solve the technical problem that new energy locomotives cannot achieve unified management.

[0006] In order to solve the above problems, the present invention provides a new energy locomotive big data management system, which includes a data acquisition module, a management module, a big data analysis module, and a display module; The data acquisition module is used to determine a data acquisition interface and acquire locomotive data in real time based on the data acquisition interface; The management module is used to manage the data acquisition interface and classify and manage the locomotive data; The big data analysis module is used to build a big data model based on the locomotive data, predict the health status and health trend of the locomotive based on the big data model, obtain prediction data, set an alarm threshold, and generate an early warning signal based on the alarm threshold and the locomotive data; The display module is used to display the locomotive data, prediction data and warning signals.

[0007] In a possible implementation, the data acquisition module includes an information acquisition unit, a locomotive position acquisition unit, and a component information acquisition unit; The information collection unit is used to collect basic information of the locomotive, wherein the basic information of the locomotive at least includes the model, serial number, manufacturer, and production date of the locomotive; The locomotive position acquisition unit is used to acquire the positioning coordinate data of the locomotive in real time through the data acquisition interface; The component information acquisition unit is used to acquire the basic information of the locomotive components, the running status data of the components during the operation of the locomotive, and the maintenance data of the components in real time through the data acquisition interface.

[0008] In a possible implementation, the basic information of the locomotive components includes the name, specification model, factory code, production date, and installation location of the locomotive components. The locomotive components include at least the battery and motor of the locomotive. The operating status data of the components during the operation of the locomotive include battery operating status data and motor operating status data. The battery operating status data includes at least the capacity, power, output voltage, output current, temperature, and ambient temperature data of the battery and the capacity, power, output voltage, output current, temperature, and ambient temperature data of the battery box and the battery charging start time, battery charging completion time, charging start power, and charging completion power. The motor operating status data includes the voltage, current, speed, torque, and temperature of the motor. The maintenance data of the components includes at least the maintenance time, maintenance content, and maintenance personnel.

[0009] In a possible implementation, the data acquisition interface includes a locomotive position interface, a battery operation data interface, a battery charging data interface, a battery box operation data interface, and a motor operation data interface.

[0010] In a possible implementation, the management module includes a data acquisition interface management unit, an information management unit, and a component management unit; The data acquisition interface management unit is used to manage the data acquisition interface configuration and maintain the parameters of the data acquisition interface; The information management unit is used to centrally manage and query the basic information of the locomotive, and update the basic information of the locomotive after modification and maintenance; The component management unit is used to classify and manage the components of the locomotive according to their functions and importance, and to formulate a reasonable component replacement plan and maintenance plan.

[0011] In a possible implementation, the parameters of the data acquisition interface include an interface protocol, an interface address, an acquisition frequency, and a binding relationship between the data acquisition interface and the locomotive.

[0012] In a possible implementation, the management module further includes a rights management unit; The authority management unit is used to configure different authorities for different users to achieve authority isolation of users, wherein the different users include system administrators, management personnel, maintenance personnel, business personnel, and customers.

[0013] In a possible implementation, the big data analysis module includes a big data model prediction unit and an early warning unit; The big data model building unit is used to build a big data platform based on the acquired life cycle operation data of the locomotive, build a health big data model based on the big data platform, and predict the health status and health trend of locomotive components based on the health big data model to obtain prediction results; The early warning unit is used to set an alarm threshold of a locomotive component, monitor the locomotive data based on the alarm threshold to obtain abnormal data, and issue an early warning signal based on the abnormal data.

[0014] In a possible implementation, the display module includes a locomotive group display unit, a locomotive display unit, and a locomotive component display unit; The locomotive group display unit is used to display the location, status and warning information of multiple locomotives; The locomotive display unit is used to display the basic data, operating data, operating status of the battery and the motor, and the warning information of the battery and the motor of a locomotive; The locomotive component display unit is used to display the composition, parameters, status of the battery pack and the operating status of the battery box.

[0015] On the other hand, the present invention also provides a new energy locomotive big data management method, which is implemented by the new energy locomotive big data management system as described above, and includes the following steps: Determine a data acquisition interface based on the data acquisition module, and acquire locomotive data in real time based on the data acquisition interface; Centrally manage the data acquisition interface based on the management module, and classify and manage the locomotive data; Building a big data model based on the locomotive data based on the analysis module, predicting the health status and health trend of the locomotive based on the big data model, obtaining prediction data, and setting an alarm threshold, and generating an early warning signal based on the alarm threshold and the locomotive data; The locomotive data, prediction data and warning signal are displayed based on the display module.

[0016] The beneficial effects of the present invention are as follows: the new energy locomotive big data management system includes a data acquisition module, a management module, a big data analysis module, and a display module. The data acquisition module is used to acquire locomotive data in real time to obtain large-scale and diversified data of the locomotive. The management module is used to manage the data acquisition interface and locomotive data. The big data analysis module is used to build a big data model to predict the health status and health trend of the locomotive. The display module is used to display the locomotive data, predicted data, and early warning signals, which timely and accurately reflects the status of the locomotive during operation, realizes the unified management of new energy locomotives, and ensures the efficient and safe operation of new energy locomotives. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the structure of an embodiment of a new energy locomotive big data management system provided by the present invention; Figure 2 A schematic structural diagram of an embodiment of a data acquisition module of a new energy locomotive big data management system provided by the present invention; Figure 3 A schematic diagram of the structure of an embodiment of a management module of a new energy locomotive big data management system provided by the present invention; Figure 4 A schematic diagram of the structure of an embodiment of a big data analysis module of a new energy locomotive big data management system provided by the present invention; Figure 5 A schematic diagram of the structure of an embodiment of a display module of a new energy locomotive big data management system provided by the present invention; Figure 6 A flowchart of an embodiment of a new energy locomotive big data management method provided by the present invention; Figure 7 A schematic diagram of visualization of locomotive group data of the new energy locomotive big data management method provided by the present invention; Figure 8 A schematic diagram of locomotive data visualization of the new energy locomotive big data management method provided by the present invention; Fig. 9 A schematic diagram of battery pack data visualization for the new energy locomotive big data management method provided by the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0019] The present invention discloses a new energy locomotive big data management system. Figure 1 This is a schematic diagram of a new energy locomotive big data management system provided by an embodiment of the present invention. Figure 1, the new energy locomotive big data management system 100 includes a data acquisition module 110, a management module 120, a big data analysis module 130 and a display module 140; The data acquisition module 110 is used to determine the data acquisition interface and acquire locomotive data in real time based on the data acquisition interface; The management module 120 is used to centrally manage the data collection interface and classify the locomotive data; The big data analysis module 130 is used to build a big data model based on the locomotive data, predict the health status and health trend of the locomotive based on the big data model, obtain prediction data, set an alarm threshold, and generate an early warning signal based on the alarm threshold and the locomotive data; The display module 140 is used to display locomotive data, prediction data and warning signals.

[0020] In some embodiments, see Figure 2 , the data acquisition module 110 includes an information acquisition unit 111, a locomotive position acquisition unit 112 and a component information acquisition unit 113; The information collection unit 111 is used to collect basic information of the locomotive, wherein the basic information of the locomotive includes at least the model, serial number, manufacturer, and production date of the locomotive; the basic information of the locomotive is like the "identity file" of the locomotive, recording the unique attributes of each locomotive; The locomotive position acquisition unit 112 is used to obtain the positioning coordinate data of the locomotive in real time through the data acquisition interface, and collect the position data of the locomotive by installing a positioning device on the locomotive; The component information acquisition unit 113 is used to obtain in real time through the data acquisition interface the basic information of the locomotive components, the operating status data of the components during the operation of the locomotive, and the maintenance data of the components. The basic information of the locomotive components includes the name, specification model, factory code, production date, and installation location of the locomotive components. The locomotive components include at least the battery and motor of the locomotive. The operating status data of the components during the operation of the locomotive include the battery operating status data of the locomotive and the motor operating status data of the locomotive. The battery operating status data includes at least the capacity, power, output voltage, output current, temperature, and ambient temperature data of the battery and the capacity, power, output voltage, output current, temperature, and ambient temperature data of the battery box and the battery charging start time, battery charging completion time, charging start power, and charging completion power. The motor operating status data includes the voltage, current, speed, torque, and temperature of the motor. The maintenance data of the components includes at least the maintenance time, maintenance content, and maintenance personnel. The data acquisition interface includes a locomotive position interface, a battery operating data interface, a battery charging data interface, a battery box operating data interface, and a motor operating data interface.

[0021] In some embodiments, see Figure 3, the management module 120 includes a data acquisition interface management unit 121, an information management unit 122 and a component management unit 123; The data acquisition interface management unit 121 is used to manage the data acquisition interface configuration and maintain the parameters of the data acquisition interface, wherein the parameters of the data acquisition interface include the interface protocol, interface address, acquisition frequency, and the binding relationship between the data acquisition interface and the locomotive; The information management unit 122 is used to centrally manage and query the basic information of the locomotive, and update the basic information of the locomotive after modification and maintenance; The component management unit 123 is used to classify and manage the components of the locomotive according to their functions and importance, and to formulate a reasonable component replacement plan and maintenance plan; The management module 120 also includes a permission management unit, which is used to configure different permissions for different users to achieve user permission isolation, where different users include system administrators, management personnel, maintenance personnel, business, and customers.

[0022] In some embodiments, see Figure 4 , the analysis module 130 includes a big data model prediction unit 131 and an early warning unit 132; The big data model prediction unit 131 is used to build a big data platform based on the acquired life cycle operation data of the locomotive, build a health big data model based on the big data platform, and predict the health status and health trend of the locomotive components based on the health big data model to obtain prediction results; The early warning unit 132 is used to set the alarm threshold of the locomotive components, monitor the locomotive data based on the alarm threshold to obtain abnormal data, and issue an early warning signal based on the abnormal data.

[0023] In some embodiments, see Figure 5 , the display module 140 includes a locomotive group display unit 141, a locomotive display unit 142 and a locomotive component display unit 143; The locomotive group display unit 141 is used to display the location, status and warning information of multiple locomotives; The locomotive display unit 142 is used to display the basic data, operation data, battery and motor operation status, and battery and motor warning information of the locomotive; The locomotive component display unit 143 is used to display the composition, parameters, status of the battery pack and the operating status of the battery box.

[0024] A specific embodiment of the present invention discloses a new energy locomotive big data management method. Figure 6 This is a flowchart of the new energy locomotive big data management method provided by the embodiment of the present invention. Figure 6, the big data management methods for new energy locomotives include: S601, determining a data acquisition interface based on a data acquisition module, and acquiring locomotive data in real time based on the data acquisition interface; S602, centrally manage the data collection interface based on the management module, and classify and manage the locomotive data; S603, constructing a big data model based on the locomotive data based on the analysis module, predicting the health status and health trend of the locomotive based on the big data model, obtaining prediction data, and setting an alarm threshold, and generating an early warning signal based on the alarm threshold and the locomotive data; S604: Display locomotive data, prediction data and warning signals based on the display module.

[0025] Among them, locomotive data is collected through the DTU device installed on the locomotive, and the data collection interface is determined according to the data form and transmission protocol. The data collection interface is deployed on the cloud server to complete the connection between the DTU device and the cloud server to obtain locomotive data in real time.

[0026] Compared with the prior art, the new energy locomotive big data management method provided in this embodiment determines the data acquisition interface based on the data acquisition module, obtains locomotive data in real time based on the data acquisition interface, obtains locomotive data in real time through the data acquisition module to obtain large-scale and diversified data of the locomotive, centrally manages the data acquisition interface based on the management module, and classifies and manages the locomotive data to facilitate more targeted management and maintenance of the locomotive, the big data analysis module builds a big data model based on the locomotive data, predicts the health status and health trend of the locomotive based on the big data model, obtains prediction data, and sets an alarm threshold, generates an early warning signal based on the alarm threshold and the locomotive data, displays the locomotive data, prediction data and early warning signal based on the display module, manages the data acquisition interface and locomotive data through the management module, builds a big data model through the big data analysis module to predict the health status and health trend of the locomotive, displays the locomotive data, prediction data and early warning signal through the display module, timely and accurately reflects the status of the locomotive during operation, realizes efficient management and analysis of large-scale and diversified data of new energy locomotives, and ensures efficient and safe operation of new energy locomotives.

[0027] In some embodiments, in step S601, a data acquisition interface is determined, locomotive data is acquired in real time based on the data acquisition interface, a positioning device, a VCU device and a DTU device are installed on the locomotive, locomotive data is acquired through the positioning device and the VCU device, and then locomotive data is collected through the DTU device, a data acquisition interface is customized and developed according to the locomotive data and the transmission protocol, and the data acquisition interface is deployed on a cloud server, that is, after the DTU device collects the locomotive data, serial port data is generated, and after the serial port data is converted into IP data through the DTU device, it is connected to the data acquisition interface through a wireless network, and the locomotive data is transmitted to the cloud server, and the locomotive data is acquired in real time by calling the data acquisition interface, so that the locomotive positioning, battery status, traction motor status and other related data can be remotely and in real time collected. The data acquisition interface includes a locomotive position interface, a battery operation data interface, a battery charging data interface, a battery box operation data interface and a motor operation data interface, and the positioning coordinate data of the locomotive is acquired through the locomotive position interface, and the battery capacity, power, output voltage, output current, battery temperature, environment during the operation of the locomotive are acquired through the battery operation data interface. The battery box operation data interface collects the capacity, power, output voltage, output current, battery box temperature and other data of each battery box that makes up the battery during the operation of the locomotive. The battery charging data interface collects battery charging data, including charging start time, charging completion time, charging start power, charging completion power, etc. The motor operation data interface collects the voltage, current, speed, torque and temperature of the motor during operation. The Tbox on-board terminal can also be installed inside each locomotive to realize the positioning and tracking function through the Tbox on-board terminal. Through the Beidou module, 4G / 5G network, and two-way CAN bus communication, the locomotive can be located and tracked, and data, historical trajectory, and blind area data can be transmitted back at regular intervals. The alarm function is realized through the Tbox on-board terminal, and its alarm functions include overspeed alarm, host removal alarm, and battery low voltage alarm. The remote control function is realized through the Tbox on-board terminal, that is, remote power off, remote restart and other functions are realized. Data collection is realized through the Tbox on-board terminal, and the collected data is sent to the cloud for reception through the TCP protocol. The collected data mainly includes locomotive status data such as motor, battery, fire protection, and main control.

[0028] In some embodiments, in step S602, the data acquisition interface is managed based on the management module. After the locomotive data is collected through the data acquisition interface, the locomotive data can be queried and the collection status can be queried. The data acquisition interface configuration is managed through the management module, that is, the protocol and data specification between the DTU device and the data acquisition interface are unified. The protocol between the DTU device and the data acquisition interface can adopt HTTP protocol, SOAP protocol or TCP / IP protocol, and the parameters of the data acquisition interface are maintained. The parameters of the data acquisition interface include interface protocol, interface address, collection frequency, and interface and locomotive binding relationship. By managing the data acquisition interface, multiple locomotives use a unified data interface to transmit data, realize the reusability of the data interface, facilitate unified management of multiple locomotives, and reduce development and maintenance costs; through the management of the data acquisition interface, the operating status of the interface can be viewed in real time, such as the interface on and off status and the latest data collection time; the locomotive data is classified and managed. First, the locomotive information is centrally managed and queried, and the basic Information is updated. By centrally managing and querying locomotive information, detailed information of a specific locomotive can be quickly retrieved, and the basic situation of the entire locomotive team can be mastered as a whole. It can also timely and accurately reflect the changes that occur in the use of the locomotive, such as maintenance records, modification conditions, etc., to ensure that the basic information of the locomotive is always kept up to date and accurate, and provide reliable data support for the operation, maintenance and management decisions of the locomotive; when parts need to be replaced, the basic information of the locomotive after modification and maintenance is updated, and the orderliness and traceability of locomotive parts management are ensured by accurately recording the replacement time and information of the new parts. Secondly, the locomotive parts are classified and managed. After collecting locomotive data, the specific information of the battery and motor of each locomotive key component is recorded. An exclusive file is established for each component through the management module, and the locomotive parts are classified according to different functions and importance factors, so as to facilitate more targeted management and maintenance. According to the operation of the locomotive and the characteristics of the parts, a reasonable parts replacement plan and maintenance plan are formulated to ensure the continuous stable operation and good performance of the locomotive.

[0029] In some embodiments, in step S603, a big data model is constructed based on locomotive data, and the health status and health trend of the locomotive are predicted based on the big data model to obtain prediction data. First, a big data platform is constructed based on locomotive data. The big data platform is constructed by collecting a large amount of full life cycle operation data of locomotive batteries and motors. The big data platform includes a data source, a data acquisition layer, a data layer, a data structure layer, and a data application layer. The data source is the place where data resources for management and analysis of the big data platform are generated. According to the type of locomotive data, it can be divided into structured data, semi-structured data, and unstructured data. The structured data mainly includes positioning data and operation data of new energy locomotives. The operation data includes battery data, motor data, operation and maintenance detection data, rescue and disaster relief data, etc. during the operation of the locomotive. Semi-structured data is mainly data files in the form of XML, JSON, etc., and unstructured data mainly includes pictures, videos, audio, and various document data. The data acquisition layer plays a basic and key role in the entire big data platform. The data acquisition layer uses data probes to accurately obtain various types of raw data, uses data stream processing technology to preliminarily organize and optimize the collected data, and uses APIs to achieve effective docking and integration with different data sources. With the help of 4G / 5G The network ensures high-speed and stable data transmission, and uses MQ message queue technology to buffer and coordinate the flow of data. Its main function is to continuously provide the entire platform with rich and diverse data sources, ensure that the platform has sufficient data foundation for subsequent analysis, processing and application, and provide solid support for upper-level analysis and decision-making; the data layer is responsible for data storage and management, using relational or non-relational databases and file systems to determine data structure definitions, while providing data access interfaces for interaction with other layers, and is responsible for maintaining data integrity and consistency; the data interface layer covers basic analysis, multidimensional analysis, data mining, real-time analysis, self-service analysis and data sharing. Basic analysis is used for preliminary understanding and exploration of data, and multidimensional analysis can deeply analyze data from multiple angles; data mining aims to discover patterns and knowledge hidden in the data; real-time analysis ensures timely processing and analysis of real-time generated data; self-service analysis gives users the ability to explore and analyze data independently; data sharing promotes the circulation and interaction of data between different systems and departments.The data interface layer builds a bridge that enables the integration and collaboration of various types of data analysis functions. On the one hand, it provides rich, accurate, and timely data support and analysis capabilities for the upper application and business logic layers, helping to make more informed decisions and actions; on the other hand, it maximizes the use and value of data through data sharing, promotes the smooth operation and efficient use of data throughout the system, and continuously explores the potential value of data to adapt to changing business needs and technological development; in the big data platform, the data application layer is in the final key position. The data application layer is mainly responsible for converting processed and analyzed data into practical applicable results and values. It integrates various specific application scenarios and functional modules, and through data-driven business applications, Apply data to actual business processes to improve efficiency and effectiveness, analyze and infer based on data to assist in making better decisions. The data application layer closely integrates the big data platform with specific business needs and goals, so that data can effectively provide strong support and promotion for enterprise operations. Secondly, build a healthy big data model based on the big data platform, predict the health status and health trends of locomotive components based on the healthy big data model, and obtain prediction results. That is, build a locomotive health status assessment standard based on the big data platform, create a healthy big data model, analyze the health status and health trends of batteries and motors through the healthy big data model, predict the health status of batteries and traction motors and their health status development trends, obtain prediction results, and then provide decision support.

[0030] By analyzing the collected locomotive positioning data, the position and movement of the locomotive are monitored in real time. Based on the data of the historical time period, the locomotive's trajectory changes can be replayed, and the operating status data of the locomotive's battery and traction motor can be collected. The changing patterns of the locomotive's battery and motor status are analyzed and presented in the form of tables and visual charts. The alarm thresholds of various indicators under the operating status of the battery and motor are set, the real-time collected data is monitored, and early warning signals are issued for abnormal data.

[0031] In some embodiments, in step S604, the display module displays locomotive data, prediction data and warning signals, and different permissions are configured for different users through the management module. According to the permissions of the users, the corresponding real-time data, regular changes, warning information and prediction data of the locomotive group, locomotive and locomotive parts are visually displayed in the new energy locomotive big data center. The visualization of the locomotive group, locomotive and locomotive parts is divided into three levels. The first level of locomotive group visualization displays the location, status and warning information of multiple locomotives. The schematic diagram of the visualization of the locomotive group data can be found in Figure 7 The second level of locomotive visualization takes a locomotive as the object and displays the basic parameters, operating data, battery and motor operating status, and battery and motor warning information of the locomotive. For a schematic diagram of locomotive data visualization, please refer to Figure 8 The third level of locomotive component visualization is based on the locomotive battery pack, showing the composition, parameters, status of each battery pack, and operating status trend of the battery pack. For a schematic diagram of battery pack data visualization, please refer to Fig. 9 .

[0032] The management module configures different permissions for different users according to different system terminals, modules and sub-functions. For example, in the first-level visualization, second-level visualization and third-level visualization of the data visualization module, the system administrator and administrator have viewing and maintenance permissions, maintenance personnel do not have any viewing and maintenance permissions, and business and customers only have viewing permissions but no maintenance permissions.

[0033] In summary, the new energy locomotive big data management system and method provided by the present invention, the new energy locomotive big data management system includes a data acquisition module, a management module, a big data analysis module, and a display module; the data acquisition module is used to determine the data acquisition interface, and obtain locomotive data in real time based on the data acquisition interface; the management module is used to manage the data acquisition interface and classify the locomotive data; the big data analysis module is used to build a big data model based on the locomotive data, predict the health status and health trend of the locomotive based on the big data model, obtain predicted data, and set the alarm threshold, and generate an early warning signal based on the alarm threshold and the locomotive data; the display module is used to display the locomotive data, the predicted data and the early warning signal, so as to realize the unified management of the new energy locomotive and ensure the efficient and safe operation of the new energy locomotive.

[0034] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A new energy locomotive big data management system, characterized in that: Including data collection module, management module, big data analysis module and display module; The data acquisition module is used to determine a data acquisition interface and acquire locomotive data in real time based on the data acquisition interface; The management module is used to manage the data acquisition interface and classify and manage the locomotive data; The big data analysis module is used to build a big data model based on the locomotive data, predict the health status and health trend of the locomotive based on the big data model, obtain prediction data, set an alarm threshold, and generate an early warning signal based on the alarm threshold and the locomotive data; The display module is used to display the locomotive data, prediction data and warning signals.

2. The new energy locomotive big data management system according to claim 1 is characterized in that: The data acquisition module includes an information acquisition unit, a locomotive position acquisition unit and a component information acquisition unit; The information collection unit is used to collect basic information of the locomotive, wherein the basic information of the locomotive at least includes the model, serial number, manufacturer, and production date of the locomotive; The locomotive position acquisition unit is used to acquire the positioning coordinate data of the locomotive in real time through the data acquisition interface; The component information acquisition unit is used to acquire the basic information of the locomotive components, the running status data of the components during the operation of the locomotive, and the maintenance data of the components in real time through the data acquisition interface.

3. The new energy locomotive big data management system according to claim 2 is characterized in that: The basic information of the locomotive components includes the name, specification model, factory code, production date, and installation location of the locomotive components. The locomotive components include at least the battery and motor of the locomotive. The operating status data of the components during the operation of the locomotive include battery operating status data and motor operating status data. The battery operating status data includes at least the capacity, power, output voltage, output current, temperature, and ambient temperature data of the battery and the capacity, power, output voltage, output current, temperature, and ambient temperature data of the battery box and the battery charging start time, battery charging completion time, charging start power, and charging completion power. The motor operating status data includes the voltage, current, speed, torque, and temperature of the motor. The maintenance data of the components includes at least the maintenance time, maintenance content, and maintenance personnel.

4. The new energy locomotive big data management method according to claim 2 is characterized in that: The data acquisition interface includes a locomotive position interface, a battery operation data interface, a battery charging data interface, a battery box operation data interface and a motor operation data interface.

5. The new energy locomotive big data management system according to claim 1 is characterized in that: The management module includes a data acquisition interface management unit, an information management unit and a component management unit; The data acquisition interface management unit is used to manage the data acquisition interface configuration and maintain the parameters of the data acquisition interface; The information management unit is used to centrally manage and query the basic information of the locomotive, and update the basic information of the locomotive after modification and maintenance; The component management unit is used to classify and manage the components of the locomotive according to their functions and importance, and to formulate a reasonable component replacement plan and maintenance plan.

6. The new energy locomotive big data management system according to claim 5 is characterized in that: The parameters of the data acquisition interface include interface protocol, interface address, acquisition frequency, and the binding relationship between the data acquisition interface and the locomotive.

7. The new energy locomotive big data management system according to claim 5 is characterized in that: The management module also includes a rights management unit; The authority management unit is used to configure different authorities for different users to achieve authority isolation of users, wherein the different users include system administrators, management personnel, maintenance personnel, business personnel, and customers.

8. The new energy locomotive big data management system according to claim 1 is characterized in that: The big data analysis module includes a big data model prediction unit and an early warning unit; The big data model building unit is used to build a big data platform based on the acquired life cycle operation data of the locomotive, build a health big data model based on the big data platform, and predict the health status and health trend of locomotive components based on the health big data model to obtain prediction results; The early warning unit is used to set an alarm threshold of a locomotive component, monitor the locomotive data based on the alarm threshold to obtain abnormal data, and issue an early warning signal based on the abnormal data.

9. The new energy locomotive big data management system according to claim 1 is characterized in that: The display module includes a locomotive group display unit, a locomotive display unit and a locomotive component display unit; The locomotive group display unit is used to display the location, status and warning information of multiple locomotives; The locomotive display unit is used to display the basic data, operating data, operating status of the battery and the motor, and the warning information of the battery and the motor of the locomotive; The locomotive component display unit is used to display the composition, parameters, status of the battery pack and the operating status of the battery box.

10. A new energy locomotive big data management method, characterized in that: The method is implemented by the new energy locomotive big data management system as claimed in any one of claims 1 to 9, comprising the following steps: Determine a data acquisition interface based on the data acquisition module, and acquire locomotive data in real time based on the data acquisition interface; Centrally manage the data acquisition interface based on the management module, and classify and manage the locomotive data; Building a big data model based on the locomotive data based on the analysis module, predicting the health status and health trend of the locomotive based on the big data model, obtaining prediction data, and setting an alarm threshold, and generating an early warning signal based on the alarm threshold and the locomotive data; The locomotive data, prediction data and warning signal are displayed based on the display module.

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